Bart, Michael and Patrick are joint first authors. Research conducted as part of MATS 6.0 in Lee Sharkey and Neel Nanda’s streams. Thanks to Mckenna Fitzgerald and Robert Krzyzanowski for their feedback!

TL;DR:

Sparse Autoencoder (SAE) latents have been shown to typically be monosemantic (i.e. correspond to an interpretable property of the input). It is sometimes implicitly assumed that they are therefore atomic, i.e. simple, irreducible units that make up the model’s computation.
We provide evidence against this assumption by finding sparse, interpretable decompositions of SAE decoder directions into seemingly more atomic latents, e.g. Einstein -> science + famous + German + astronomy + energy + starts with E-
We do this by training meta-SAEs, an SAE trained to reconstruct the decoder directions of a normal SAE.
We argue that, conceptually, there’s no reason to expect SAE latents to be atomic - when the model is thinking about Albert Einstein, it likely also thinks about Germanness, physicists, etc. Because Einstein always entails those things, the sparsest solution is to have the Albert Einstein latent also boost them.
Key results
- SAE latents can be decomposed into more atomic, interpretable meta-latents.
- We show that when latents in a larger SAE have split out from latents in a smaller SAE, a meta SAE trained on the larger SAE often recovers this structure.
- We demonstrate that meta-latents allow for more precise causal interventions on model behavior than SAE latents on a targeted knowledge editing task.
We believe that the alternate, interpretable decomposition using MetaSAEs casts doubt on the implicit assumption that SAE latents are atomic. We show preliminary results that MetaSAE latents have significant ovelap with latents in a normal SAE of the same size but may relate differently to the larger SAEs used in MetaSAE training.

We made a dashboard that lets you explore meta-SAE latents.

Terminology: Throughout this post we use “latents” to describe the concrete components of the SAE’s dictionary, whereas “feature” refers to the abstract concepts, following Lieberum et al.

Introduction

Mechanistic interpretability (mech interp) attempts to understand neural networks by breaking down their computation into interpretable components. One of the key challenges of this line of research is the polysemanticity of neurons, meaning th...

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